Genomics of rapid adaptation to antibiotics
Ontology highlight
ABSTRACT:
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| ERR348742_1.fastq.gz | Fastqsanger.gz | |||
| ERR348742_2.fastq.gz | Fastqsanger.gz | |||
| ERR348743_1.fastq.gz | Fastqsanger.gz | |||
| ERR348743_2.fastq.gz | Fastqsanger.gz | |||
| ERR348744_1.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 128 |
Similar Datasets
Project description:FIBR - study to investigate rapid genomic adaptation in experimental populations of guppies (Poecilia reticulata)
| PRJEB42705 | ENA
Project description:Bacterial rapid adaptation to alkaline environments
| PRJNA1125898 | ENA
Project description:To investigate the rapid adaptation mechanism of Bacillus thuringiensis in an alkaline environment, we have employed whole genome microarray expression profiling as a discovery platform to identify the difference of gene expression between normal condition and alkaline condition.
2024-06-24 | GSE270261 | GEO
Project description:Evolutionary adaptation can be extremely fast, especially in response to high selection intensities. A prime example is the surge of antibiotic resistance in bacteria. The genomic underpinnings of such rapid changes may provide information on the genetic processes that enhance fast responses and the particular trait functions under selection. Here, we use experimentally evolved Escherichia coli for a detailed dissection of the genomics of rapid antibiotic resistance evolution. Our new analyses demonstrate that amplification of a sequence region containing several known antibiotic resistance genes represents a fast genomic response mechanism under high antibiotic stress, here exerted by drug combination. In particular, higher dosage of such antibiotic combinations coincided with higher copy number of the sequence region. The amplification appears to be evolutionarily costly, because amplification levels rapidly dropped after removal of the drugs. Our results suggest that amplification is a scalable process, as copy number rapidly changes in response to the selective pressure encountered. Moreover, repeated patterns of convergent evolution were found across the experimentally evolved bacterial populations, including those with lower antibiotic selection intensities. Intriguingly, convergent evolution was identified on different organizational levels, ranging from the above sequence amplification, high variant frequencies in specific genes, prevalence of individual nonsynonymous mutations to the unusual repeated occurrence of a particular synonymous mutation in Glycine codons. We conclude that constrained evolutionary trajectories underlie rapid adaptation to antibiotics. Of the identified genomic changes, sequence amplification seems to represent the most potent, albeit costly genomic response mechanism to high antibiotic stress.
| S-EPMC4079197 | biostudies-literature
Project description:Rapid parallel adaptation despite gene flow in silent crickets
| PRJEB39125 | ENA
Project description:The present project investigates transcriptomics changes in laboratory mutants of Salmonella typhimurium SL1344 obtained by pre-exposure to biocides, including triclosan (TRI), benzalkonium chloride (BZC), and chlorexidine (CHX), and antibiotics including Ampicillin (AP) and ciprofloxacin (Cip), as well as in natural isolates selected for their resistance to these same biocides. Changes in gene expression were investigated using a 12k combimatrix customarray, design-based on the genome of SL1344 as well as a variety of genes of plasmid origins.
2016-06-30 | E-MTAB-2554 | biostudies-arrayexpress
Project description:Transfer RNAs mediate rapid adaptation of Escherichia coli under oxidative stress
| PRJNA266390 | ENA
Project description:Human activity is altering the environment at a rapid pace, challenging the adaptive capacities of genetic variation within animal populations. Animals also harbor extensive gut microbiomes, which play diverse roles in host health and fitness and may help expanding host capabilities. The unprecedented scale of human usage of xenobiotics and contamination with environmental toxins describes one challenge against which bacteria with their immense biochemical diversity are particularly suited to offer solutions. To explore the paths leading to bacteria-assisted rapid adaptation, we used Caenorhabditis elegans harboring a defined microbiome, and the antibiotic neomycin as a model toxin, harmful for the worm host and neutralized to different extents by microbiome members. Worms exposed to neomycin showed delayed development and decreased survival but were protected when colonized by neomycin-resistant members of the microbiome. Through a combination of 16S gene sequencing, counting of live bacteria and behavioral assays we identified two distinct mechanisms that facilitated adaptation: gut enrichment for a neomycin-modifying strain driven by altered bacterial competition; and host avoidance behavior, which depended on the stress-activated KGB-1/JNK and enabled preference of neomycin-protective bacteria. The straightforwardness of these mechanisms suggests that bacteria-assisted host adaptation may be more common than currently appreciated, protecting animals from novel stressors. However, gut remodeling may also cause dysbiosis, and additional experiments identified fitness trade-offs including increased susceptibility to infection as well as metabolic remodeling. Extending these results to other toxins suggests yet unaccounted-for microbiome-dependent long-term consequences of toxin exposure.
2023-11-09 | GSE246966 | GEO
Project description:Identifying the mode of action (MOA) of antibacterial compounds is the fundamental basis for the development of new antibiotics, and the challenge increases with the emerging secondary and indirect effect from antibiotic stress. Although various omics-based system biology approaches of defining antibiotic MOA are currently available, they still need improved throughput, accuracy and comprehensiveness. Using high resolution accurate mass (HR/AM) based proteomics, we present here a comprehensive reference map of proteomic signatures of Escherichia coli under antibiotics challenge. With state-of-the-art label-free approach, we quantified > 1,500 protein groups in response to 19 FDA-approved antibiotics. Applying several machine learning techniques, we derived a panel of 14 proteins that can be used to classify antibiotics into different MOAs with nearly 100% accuracy. Interestingly, these proteins tend to mediate diverse bacterial cellular and metabolic processes. Transcriptomic level profiling correlates well with changes in protein expression in discriminating different antibiotics. Such expression signatures will aid future studies in identifying MOA of unknown compounds and facilitate the discovery of novel antibiotics. In summary, our study offers a practical approach for effective and rapid proteomic profiling, establishes a high quality reference compendium of microbial proteome in response to a wide range of antibiotics exposure, and provides a previously undescribed group of proteins and RNAs that allows rapid antibiotic classification and MOA determination.
2020-07-23 | PXD016074 | Pride
Project description:Identifying the mode of action (MOA) of antibacterial compounds is the fundamental basis for the development of new antibiotics, and the challenge increases with the emerging secondary and indirect effect from antibiotic stress. Although various omics-based system biology approaches of defining antibiotic MOA are currently available, they still need improved throughput, accuracy and comprehensiveness. Using high resolution accurate mass (HR/AM) based proteomics, we present here a comprehensive reference map of proteomic signatures of Escherichia coli under antibiotics challenge. With state-of-the-art label-free approach, we quantified > 1,500 protein groups in response to 19 FDA-approved antibiotics. Applying several machine learning techniques, we derived a panel of 14 proteins that can be used to classify antibiotics into different MOAs with nearly 100% accuracy. Interestingly, these proteins tend to mediate diverse bacterial cellular and metabolic processes. Transcriptomic level profiling correlates well with changes in protein expression in discriminating different antibiotics. Such expression signatures will aid future studies in identifying MOA of unknown compounds and facilitate the discovery of novel antibiotics. In summary, our study offers a practical approach for effective and rapid proteomic profiling, establishes a high quality reference compendium of microbial proteome in response to a wide range of antibiotics exposure, and provides a previously undescribed group of proteins and RNAs that allows rapid antibiotic classification and MOA determination.
2020-07-23 | PXD016001 | Pride